Date of Publication

10-19-2022

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Engineering

Subject Categories

Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Argel A. Bandala

Defense Panel Chair

Ana Antoniette C. Illahi

Defense Panel Member

Dino Dominic F. Ligutan
John Anthony C. Jose

Abstract/Summary

This paper presents the design and development of a Drone Team system that can be manipulated and controlled through hand gestures for object transportation. This system uses the Leap Motion Controller and Leap Motion SDK to read and measure hand data that can be interpreted and translated into gestures and commands. Additionally, the system was made considering the use of Crazyflie 2.0 nano-quadcopters in the drone team. This system is made and developed using MATLAB and Simulink alongside Robotics Operating System (ROS) and Gazebo. In coordination with Gazebo, ROS allows the drone team and the payload to be visually simulated while keeping track of location data necessary for control and data collection. MATLAB and Simulink are used to implement the various controllers on a Crazyflie 2.0 nano-quadcopter. Simulink would accept the data from ROS and process it based on the hand gesture command transmitted from the Leap Motion Controller. The Simulink model will then develop new desired destination data that will be transmitted to ROS with the change reflected in Gazebo. To test the system, the Leap Motion Controller will have its response time tested, and the system will be used to fly the drone team along three specified routes to test the ability of the system to control the drone team and payload. The tests show that the system can accurately and precisely control the drone team using hand gestures recorded from the Leap Motion Controller. Future studies are recommended to implement this system using real hardware outside simulations.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Drone aircraft—Control systems; Gesture recognition (Computer science)

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Embargo Period

12-9-2022

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